%
%\begin{figure}[ht]
%\centering
%\includegraphics[width=\plotscalebig]{images/fb_map.jpg}
%    \caption{Map of social connections between Facebook users living across
%      different cities over the planet. The geographic and politic structure
%    of world countries is evident at a local scale, while long-distance social ties
%    reveal how online relationships span borders and oceans.}
%    \label{fig:fb_map}
%\end{figure}

\begin{savequote}[8cm]
The First Law of Geography is: everything is related to everything else, but
near things are more related than distant things.
\qauthor{Waldo R. Tobler}
\end{savequote}

\chapter{Introduction}
\label{ch:introduction}
% 1 - Popularity of OSNs
The popularity of online social networking services has grown at an extraordinary
pace over the last years, dramatically altering how hundreds of millions of
users spend their time. The numbers are overwhelming: to date, Facebook has
more than 900 million active users, with half of them using the service on a
daily basis, while Twitter has more than 350 million users; YouTube and LinkedIn
have similarly large user bases, while dozens of smaller services
boast millions of active users. 

The importance of online social networks is growing in parallel with their
popularity: as users spend more and more time socialising online, service
providers can offer sophisticated features such as targeted advertising,
personalised recommendations and content delivery. As a result, 
it becomes of crucial significance to study and understand the online
behaviour of such users and the
characteristics of the social connections that bind them. Also, the
need to design and build systems and applications that revolve around
online social connections has sparked research into understanding
the structure of online ties among users. 
          
% 2 - Main results about OSNs
These attempts have often adopted methods of complex
network~\cite{AB02:networks, BLMC06:report} and social network
analysis~\cite{WF94:sna}, combining them with algorithmic techniques drawn from
computer science to manage massive social graphs.  Thanks to the
availability of large-scale data about online social interactions, it is
now possible to make sense of billions of friendship connections and to infer
notable patterns and useful properties.

Some seminal works have characterised the structure of these
online social networks, finding common properties which appear across different
social services.  For instance, heavy-tailed degree distributions,
where a non-negligible fraction of users have many 
social connections, and the presence of locally dense social communities 
appear as the most significant traits of many different online social
networks~\cite{MMG07:measurement, AHK07:osn}. Other work has focussed on
studying the evolution of online social networks over time, trying to reproduce
their observed properties with generative models that are inspired by mechanisms
purportedly driving user behaviour~\cite{KNT06:structure, LBKT08:microscopic}. 

% 3 - Applications
In general, studying online social networks has allowed researchers to extend
the scope of traditional social network analysis, scaling up to millions of
individuals and billions of social links.  The combination of large-scale data
analysis, insights provided by sociological theories and problems arising from
system engineering has resulted in a plethora of applications and systems that
mine online social interactions to provide suggestions, offer recommendations
and filter information. This has impacted the Web in an unprecedented way, as
these features are profoundly different from the predominantly static content,
lacking any personalisation, that was available to users only a decade
ago.

In fact, insights into the properties of online social services can 
be exploited to design novel applications 
%which take advantage of the
%information provided by users 
that provide recommendations about
items~\cite{Gol08:trust}, answer Web search queries~\cite{EC08:socialsearch,
HK10:aardvark} and reduce spam~\cite{GKF06:re}, among many other examples.
Information related to online social ties can even be used to improve existing
distributed systems and applications: for instance, by taking into account how
people use online social services to share and consume content items, it
becomes possible to optimise delivery and storage of online
content~\cite{THT12:tailgate}.

% 4 - A new revolution: the mobile Web
More recently, the widespread adoption of powerful mobile devices has led to a
dramatic change in the way the Web is accessed. In particular, every day
hundreds of millions of individuals use a smartphone to interact online with
their friends.  The launch of new operating systems for mobile devices has
drastically reshaped which applications and services are available to end
users.  Both Google's Android and Apple's iOS, the two most successful
smartphone platforms to date, offer application stores where developers can publish, and
sell, their mobile applications. The abundance of such applications
further stimulates the adoption of mobile devices: the overall effect is that
this field is developing fast and in many directions. 

An important related aspect is the increased access to social networking
services through mobile devices: mobile users spend more minutes every day
interacting with social applications than desktop
users~\cite{Mas10:mobile}~\footnote{As early as in 2010 mobile users were using
  Facebook on their mobile devices on average for 45 minutes a day, while
    desktop users only for 32 minutes.}.
   % More information is available here
   % \url{http://www.comscore.com/Press_Events/Presentations_Whitepapers/2010/GSMA_Mobile_Media_Metrics_Mobile_World_Congress_Seminar}}.
    Simultaneously, mobile Web access has caused a substantial shift in the
    feasibility of pervasive and ubiquitous services. 

The deployment of location-based services has been made possible by the
location-sensing capabilities of these devices; they are able to generate
location-tagged information and enable users to share their physical
whereabouts. As a result, online services are increasingly becoming
\textit{location-aware}.

%Since these devices offer location-sensing capabilities, the ability
%to share where the user is, to generate location-tagged information and to
%search for it makes the deployment of location-based services possible. As a
%result, online services  are increasingly becoming \textbf{location-aware},
%allowing users to share information about their geographic whereabouts.  
   
% Mobile phones and location-sensing technologies
\section{How geography affects online social networking}
\label{sec:geography}
The combination of the upsurging popularity of online social networking,
    especially on mobile platforms, and the rise of mobile location-based
    services allows us to
merge together two facets of user behaviour that were previously difficult to
connect, \textbf{adding a crucial spatial dimension to online social networking
services}.
For the first time, the wealth of information about online social interactions
can be augmented with geographic information. Online social networks were
previously studied ignoring their spatial properties, as these were not
accessible. Now, instead, users can be considered to be embedded in a data-rich
geographic space.

%For the first time, the wealth of information about online social interactions can
%be augmented with geographic information: hence, whereas online social networks
%were previously studied by ignoring their spatial properties, which were not
%accessible, users appear now embedded in a data-rich geographic space. 

% Location-based services
\subsection{The rise of location-based mobile services}
This connection between social and local services has been epitomised by
location-based online social services such as
Foursquare\furl{www.foursquare.com}, Brightkite\furl{www.brightkite.com} and
Gowalla\furl{www.gowalla.com}, which have attracted millions of users in recent years. These services are
targeting a mainly mobile user audience: they are based on the concept of
disclosing the presence of the user at a particular venue, broadcasting 
such notifications to friends. It is crucial to stress that not only the
geographic location of each user is revealed to these services, but also a
detailed set of additional data related to individual places:  for instance,
users could disclose that they are in a stadium, visiting a museum or spending
time in a cafeteria.

At the same time, reviews, tips or other information related to such places 
can be generated and shared. Therefore, a vast and
detailed user-generated catalogue of venues is continuously growing within each
service, compiled by users themselves and providing fine-grained data about 
where people go. Places, with their simultaneous online and
offline presence, represent a new entity that drives and shapes user behaviour,
bridging the gap between physical location and online
activity~\cite{CTH11:places}.

More generally, all online social services are increasingly becoming location-aware,
allowing users to create and access information about their geographic
whereabouts. The trend is progressively going from specialised providers
offering \textit{location-as-a-service} to a widespread new concept of
\textit{location-as-a-feature}, where every online social platform integrates
geographic information into their services. For instance, Facebook recently
introduced a new feature allowing every single piece of information
generated on the service, being it a status update, a photo or a
notification from a third-party application, to be tagged with a specific
spatial location. Hence, spatial details related to online social activities
become progressively more available and exploitable.

\subsection{The effect of geographic space on online social ties}
Among the many interesting research questions sparked by the availability of
spatial data on online social services, a fundamental one is whether geographic
space affects social interactions taking place on the Web.

%% Spatial networks
Systems where space and distance constrain connections between networked
entities have been extensively studied, like in
transportation networks~\cite{KT06:transportation}, Internet router
connections~\cite{YJB02:internet, BGG03:internet}, power grids~\cite{AAN04:grid} and urban road
networks~\cite{CSLP06:cities}. 
%These and other systems where nodes are embedded
%in a metric space are conventionally represented and studied as \textbf{spatial
%networks}~\cite{Bar11:spatial}.   
% Distances imposes costs
In general, metric distance directly influences these systems
by imposing higher costs on the connections between distant entities.
When there is a cost associated with link length, the appearance, and the
persistence, of longer links is usually compensated by some other advantage. As
an example, long-distance commercial flights are often directed to
well-connected airport hubs. 
%The structure of the resulting network is therefore
%heavily influenced by the location of the nodes and the spatial distance between
%them.

% And social nets?
However, social networks have been largely studied from a purely topological
perspective, focussing mainly on the  structure of the graph.
Some sociologists have studied the effect of geographic distance on social ties
before the advent of online social services,  with the underlying expectation
that most individuals would try to minimise the efforts to maintain a friendship link
by interacting more with their spatial neighbours.  
This would be in accordance with the broad ``Principle of Least Effort''
theorised and proposed by Zipf to explain multiple facets of human
behaviour~\cite{Zip49:effort}. Individuals could be less likely to meet people
who live further away because overcoming distance needs more time or more money, in other
words, more effort.

In fact, as early as 1941 Stewart observed an inverse relationship between
distance and the likelihood of friendship between college
students~\cite{Ste41:distance}. Similar statistical regularities have been later
observed in new housing developments~\cite{FSB63:housing}, residences for the
elderly~\cite{NL75:propinquity} and urban interactions~\cite{ML76:sizes}.
Nonetheless, the connection costs imposed by spatial distance may not be
important in social systems, particularly when focussing on online interactions.
The Internet and, in general, other communication technologies may potentially
lessen  the costs associated with social interaction, removing
geographic barriers and reducing overhead. 

\subsection{An historical perspective}
% The global village and the death of distance
As McLuhan theorised in
1962~\cite{Mcl62:media}, years before the inception of the Internet, the
enhanced transmission speed of information given by modern mass media would turn
the world into a ``Global Village''. Thirty years later, such a concept  became
widely popular thanks to the birth of the Web, which fostered the idea that
people can communicate with ease and simplicity as a single, planetary
community.  

It is reasonable to say that, thanks to the Web, people now are connected, and
keep in touch, with greater simplicity and proficiency than at any time in the past. As proposed by
Cairncross, spatial distance may finally cease to play a r\^{o}le because of the
increasing availability of affordable long-distance travel and cheap
communication channels, resulting in the inevitable ``Death of
Distance''~\cite{Cai01:distance}, while  other scholars have similarly
discussed the ``End of Geography''~\cite{TL88:geography}. The implied
consequence is that in this new scenario the process of friendship formation
might easily become completely disentangled from spatial
distance~\cite{Gra98:end}.

% Always the same story...
Interestingly, similar arguments had been already put forward when other technological
breakthroughs were made. For instance, the introduction of the telegraph in
1844, with an initial 40-mile link between Washington and Baltimore, provided
for the first time the effective separation of communication from
transportation, freeing the transmission of information from the constraints of
geography, as discussed by Carey~\cite{Car89:communication}. This idea goes back
to Cooley, who wrote in 1894 that ``Space -- distance  -- as an obstacle to
communication has so nearly been overcome that it is hardly worth
considering''~\cite{Coo94:transportation}. 

Similar considerations can be made about the reactions sparked by the
introduction of the telephone or the radio: common people and academic scholars
anticipated a far-reaching revolution, bound drastically to alter how
individuals would communicate with each other. Yet, as it became apparent after
each individual innovation, a new communication technology hardly cancels out or
completely replaces existing systems. Instead, it is easily adopted to maintain
and nurture social communication channels that were already in place:
face-to-face contacts and the shared experience of spatial locality remain
dominant across communication media~\cite{HW01:netville}. 

% Glocalization... distance is still here!
Similar reasoning might apply to social interactions on the Web: they
could reflect social ties and contacts that develop and exist through other
communication channels, such as face-to-face encounters or phone calls.  The
effect of distance on such social ties would then be still important, even if
online communication tools are widely available. In reality, precisely because of
the latest technological changes in travel and communication, evidence suggests
that social groups have become ``glocalised''~\cite{WH99:glocal}, with both
extensive short-range links and occasional long-distance relationships.  Even
more convincingly, some initial results clearly demonstrate that online social
connections are more likely to appear at shorter geographic
distances~\cite{LNKR05:routing, BSM10:findme}. 

As the death of distance seems postponed, space and proximity might continue to
play a pivotal r\^{o}le on the Web, influencing whom individuals connect to and
how they interact with others. Thanks to the wealth of geographic information
increasingly available, it is possible to understand the
effect that space and distance have on online social services. This is likely
to provide a more complete picture of social interactions on the
Web, with important and far-reaching implications. In addition, as the relative
importance of the Web  grows, this knowledge might shed more light on
social behaviour in a broader sense.


\section{Potential implications}
\label{sec:motivations}
Augmenting social structure with geographic information adds a new dimension to
social network analysis and a large number of theoretical investigations and
practical applications can be pursued for online social systems, with many
promising  outcomes.

From one point of view, spatial information can
help to explain social phenomena taking place online, such as the
creation of friendship ties or the spreading of information. Even though the structure and the dynamics of social
networks have been under scrutiny for many years~\cite{CFL09:social, WF94:sna},
only a few works have addressed how geographic distance affects online
social ties~\cite{LNKR05:routing, BSM10:findme}.  These initial results
still leave untouched issues such as how online users establish new social
connections over space and  whether their online interactions are affected by
distance.
Similarly, users could be characterised by their
preference towards global, long-range interactions rather than towards
local, short-distance online ties, in order to classify their behaviour and
profile them.  
%Another interesting outcome may be the design of new
%algorithms for community detection which rely both on social and on
%geographic information.

On the other hand, location-sharing on online services opens possibilities
for new applications and systems. 
%The availability of the exact geographic
%location of users is now widespread, thanks to the adoption of mobile computing
%and communication devices.  
   Details about the type of places where
individuals go are increasingly available, providing rich information
about user preferences and choices. Applications such as local search, content
recommendation and advertising would greatly benefit from such geographic
information. Search queries about local content could be targeted to
nearby users, while both advertising and recommender systems could better profile
users by knowing how their social ties stretch over space, thus improving their
accuracy. Moreover, information
about social links, content consumption and geographic location can reveal  how
tastes and interests disseminate over an online social service. Some potential
applications of these ideas include targeted advertisement, more effective
content spreading (e.g. shop promotions, local news, job openings) and
even local activism and advocacy.

Finally, large-scale systems would greatly profit from a better knowledge of how
online users are connected over space and how information spreading over space
creates demand for content and services around the planet.  In particular, with
the recent rising interest in cloud services~\cite{Hay08:cloud} and content
delivery networks~\cite{Lei09:cdn}, it has become extremely important to
understand the geographic patterns of traffic requests. A
challenging problem is to understand  whether it is possible to improve the
design of such systems by exploiting the geographic properties of social
processes. For instance, popularity of content can be geographically and
temporally characterised to devise new strategies for replica placement and
caching.  

\section{Thesis and its substantiation}
As we have discussed, gaining knowledge about how geographic space influences
online social services could be of great importance to understand better many
research problems and to improve systems related to these services. The effect
of geographic distance seems still to be present in the online world: a more
complex and broad research question regards how spatial and social factors
simultaneously influence the structure of online social networks and the dynamic
processes that take place on them. Closely related to this theme is the problem
of exploiting the spatial dimension of online user behaviour
to provide better and more useful features in online social
networking services and to devise new systems and application.

Consequently, the \textbf{thesis of this dissertation} is that \textit{the study of
the spatial characteristics of online social interactions is useful to
provide a more comprehensive understanding of their structure and
to build more efficient and effective systems and applications on top of them}.

We substantiate this statement with two closely related threads
of research. First, we aim to expand the understanding of the spatial
properties of online social networks, focussing on measuring, analysing and
modelling such properties and their connection to social patterns. Second,
we plan to demonstrate that such spatial characteristics can be used in the design
of new systems and applications related to online social networking services.

\section{Contributions and chapter outline}
This thesis offers  three major contributions: firstly, the measurement and
the analysis of social and spatial properties of online social
services, secondly, the study of models which capture the spatial and
social properties of user behaviour on such services,  and finally the design
and the evaluation of applications and systems that exploit spatial and
geographic information in online social networks.

As we have considered, the impact of spatial distance on online social networks
seems still to be important, even though the Internet and the Web allow individuals to
communicate easily and cheaply. As a consequence, the properties usually observed in online 
social networks could be influenced by geographic distance in a variety
of different ways. Hence, in Chapter~\ref{ch:social_nets} we introduce and
explore the properties of online social services,  discussing the r\^{o}le of
space in  shaping them. We also examine whether
location-sharing features, which reveal the spatial patterns of online social
interactions, might be changing how users engage with online social
platforms. This discussion provides insights into why the spatial properties of
these online services are of significant importance to understand better
online user behaviour and to build related systems.

The rest of the dissertation presents our novel contributions, which are
summarised as follows:

\begin{itemize}
\item In Chapter~\ref{ch:structure} we discuss the effect that spatial factors
have on online social platforms through a comparative study of the spatial
properties of the social graphs arising among users of popular online services.
We exploit location data available on such services to embed users in 
geographic space,  studying the resulting social networks as spatial networks.
We define two \textbf{randomised null models} of the social graph that take into
account either only the spatial properties or the social properties of the
original graph: this allows us to discern what characteristics we would observe
if only spatial, or social, factors were in place. Using these two null
models we discuss the interplay between the spatial and social dimensions,
       which generates a wide heterogeneity of properties across different users. We
       also propose two new network measures, \textbf{node locality} and the
       \textbf{geographic clustering coefficient}, which help to differentiate
       users with respect to their preference for short-range or
       long-distance ties. 
  
\item In Chapter~\ref{ch:model} we aim to understand the temporal evolution of
an online social network and its spatial properties with a longitudinal study of
a real service. Our goal is to define basic evolutionary models that can reproduce
the social and spatial patterns observed in the real data and the properties
discussed in Chapter~\ref{ch:structure}. We show that social
factors and spatial distance simultaneously influence the establishment of new
user connections: this can be modelled as a \textbf{gravitational attachment} process that mimics the
attraction forces between physical bodies influenced by mass and distance. At
the same time, we note that  triadic closure is also strongly shaping the
creation of social links, although this process appears to be driven purely by
social factors.  These findings allow us to propose a new \textbf{gravitational
  model of network growth}, which is able to reproduce the social and spatial
  properties observed in real networks. We further discuss how our new model
  compares to other frameworks previously introduced to study spatial networks.

\item In Chapter~\ref{ch:prediction} we explore one practical application that
takes advantage of spatial data available on online social networks: \textbf{link
prediction}.  Link prediction systems have been largely adopted to recommend new
friends in online social networks using data about social interactions. We
propose to exploit an additional source of information: the places people visit.
We study the problem of designing a link prediction system for
online location-based social networks. We investigate how users create new
connections over time and we study the relative link prediction space: we find that about 30\% of new
links are added between \textbf{``place-friends''}, i.e.,  between users who visit the same
places. We show that this prediction space can be made 15 times smaller, while
still 66\% of future connections can be discovered. Finally, we define new prediction
features based on the properties of the places visited by users, which are able
to discriminate potential future links among them.
Building on these findings, we describe a supervised learning
framework which exploits these prediction features to predict new links between
friends-of-friends and place-friends, offering high link prediction performance. 


\item In Chapter~\ref{ch:caching} we explore a different application that
benefits from the constraints  imposed by spatial distance on online social
connections: \textbf{video content delivery on a planetary scale}.  
%In fact, providers
%such as YouTube offer easy access to multimedia content to millions, generating
%high bandwidth and storage demand on the Content Delivery Networks they rely
%upon. 
More and more, the diffusion of content items happens on online social
networks, where social cascades can be observed when users increasingly re-post
links they have received from others.  
%We propose to exploit geographic
%information extracted from social cascades to improve caching of multimedia
%files in a content delivery network.  
We take advantage of the fact that such social
cascades can propagate in a geographically limited area to discern whether an
item is spreading locally or globally. This informs cache replacement policies
used in content delivery networks,
     which utilise this information to ensure that content %relevant to a cascade
     is kept close to the users who may be interested in it.  
     We build a proof-of-concept geographic model of a distributed content delivery
     network and we simulate its performance on real traces; our evaluation shows that we 
improve cache hits by up to 70\% with respect to cache policies
     without geographic and social information.
%     We validate our
%     approach by tracking social cascades of YouTube links over Twitter: we then
%     build a proof-of-concept geographic model of a distributed content delivery
%     network and we simulate its performance.  Our evaluation shows that we are
%     able to improve cache hits by up to 70\% with respect to cache policies
%     without geographic and social information.
\end{itemize}

To conclude, in Chapter~\ref{ch:conclusion} we discuss and summarise the insights
offered by this dissertation and we explore their consequences, presenting
directions for further research.

\section{List of publications}
During the course of my Ph.D. I have had the following 20 publications.
Thanks to many fruitful
collaborations, I had a chance to contribute to several different projects:
hence, not all the following works contribute to this dissertation. In more 
detail, this introduction is inspired by [Sce11], Chapter~\ref{ch:structure} draws from [SMML10a] and [SNLM11],
Chapter~\ref{ch:model} is based on [ASM12], Chapter~\ref{ch:prediction} is based
on [SNM11] and Chapter~\ref{ch:caching} is inspired by [SMMC11]. 


\paragraph{Works related to this dissertation}
\begin{description}

\item[\textbf{[SMML10a]} ]
Salvatore Scellato, Cecilia Mascolo, Mirco Musolesi, Vito Latora.
  Distance Matters: Geo-social Metrics for Online Social Networks.
  \emph{{Proceedings of the Third Workshop on Online Social Networks (WOSN 2010)}},
  co-located with USENIX, (Boston, Massachusetts, USA), June 2010.

\item[\textbf{[SMMC11]} ]
Salvatore Scellato, Cecilia Mascolo, Mirco Musolesi, Jon Crowcroft.
  Track Globally, Deliver Locally: Improving Content Delivery Networks
  by Tracking Geographic Social Cascades. \emph{{Proceedings of the 20th World Wide
  Web Conference (WWW 2011)}}, (Hyderabad, India), March 2011.

  \textbf{Named ``Publication of the Year 2011'' by the Cambridge Computer
    Lab Ring.}

\item[\textbf{[SNLM11]} ]
Salvatore Scellato, Anastasios Noulas, Renaud Lambiotte, Cecilia Mascolo.
  Socio-Spatial Properties of Online Location-Based Social Networks.
  \emph{{Proceedings of the Fifth International AAAI Conference on Weblogs and Social
  Media (ICWSM 2011)}}, (Barcelona, Spain), July 2011.

\item[\textbf{[SNM11]} ]
Salvatore Scellato, Anastasios Noulas, Cecilia Mascolo. Exploiting
  place features in link prediction on location-based social networks.
  \emph{{Proceedings of the 17th ACM SIGKDD International Conference on Knowledge
  Discovery and Data mining (KDD 2011)}}, (San Diego, California, USA), August 2011.

\item[\textbf{[Sce11]} ]
Salvatore Scellato. Beyond the Social Web: The Geo-Social Revolution.
\emph{ACM SIGWEB newsletter}, Autumn 2011 issue. 

\item[\textbf{[ASM12]} ]
Miltiadis Allamanis, Salvatore Scellato, Cecilia Mascolo. 
Evolution of a Location-based Online Social Network: Analysis and Models. 
\emph{{Proceedings of the 12th ACM  International 
   Internet Measurement Conference (IMC 2012)}}, (Boston, Massachusetts, USA),
   November 2012.

\end{description}

\paragraph{Other works}

\begin{description}

\item[\textbf{[DEM+10]} ] 
Vladimir Dyo, Stephen~A. Ellwood, David~W. Macdonald, Andrew Markham, Cecilia
  Mascolo, Bence P\'{a}sztor, Salvatore Scellato, Niki Trigoni, Ricklef Wohlers,
  Kharsim Yousef. Evolution and Sustainability of a Wildlife
  Monitoring Sensor Network. \emph{{Proceedings of the Eighth ACM Conference on Embedded
  Networked Sensor Systems (SenSys 2010)}}, (Z\"{u}rich, Switzerland), November 2010.

\item[\textbf{[TSM+10]} ]
John Tang, Salvatore Scellato, Mirco Musolesi, Cecilia Mascolo, Vito
  Latora. Small-world behavior in time-varying graphs. \emph{{Physical Review
    E}} \textbf{81} (2010), no.~5, 055101(R).

\item[\textbf{[SM11]} ]
Salvatore Scellato, Cecilia Mascolo. Measuring User Activity on an
  Online Location-based Social Network. \emph{{Proceedings of the Third
  International Workshop on Network Science for Communication Networks
  (NetSciCom 2011)}}, co-located with INFOCOM 2011, (Shanghai, PRC), April
  2011.

\item[\textbf{[SLM+11a]} ]
Salvatore Scellato, Ilias Leontiadis, Cecilia Mascolo, Pritwish Basu, 
  Murtaza Zafer, Understanding Robustness of Mobile Networks through
  Temporal Network Measures, \emph{{Proceedings of the 30th IEEE International
  Conference on Computer Communications (INFOCOM 2011)}}, mini-conference track,
  (Shanghai, PRC), April 2011.

\item[\textbf{[SMML10b]} ]
Salvatore Scellato, Cecilia Mascolo, Mirco Musolesi, Vito Latora. On
  Nonstationarity of Human Contact Networks. \emph{{Proceedings of the Second 
  Workshop on Simplifying Complex Networks for Practitioners (SIMPLEX 2010)}},
  co-located with ICDCS 2010, (Genoa, Italy), June 2010.


\item[\textbf{[SMM+11]} ]
Salvatore Scellato, Cecilia Mascolo, Mirco Musolesi, Vito Latora, Andrew~J.
  Campbell. NextPlace: A Spatio-Temporal Prediction Framework for
  Pervasive Systems. \emph{{Proceedings of the Ninth International Conference on
  Pervasive Computing (Pervasive 2011)}}, (San Francisco, California, USA), June
  2011.

\item[\textbf{[NSMP11]} ]
  Anastasios Noulas, Salvatore Scellato, Cecilia Mascolo, Massimiliano
  Pontil. Exploiting Semantic Annotations for Clustering Geographic Areas and Users in
  Location-based Social Networks. \emph{{Proceedings of the Third Workshop on Social
    Mobile Web (SMW 2011)}},
  co-located with ICWSM 2011, (Barcelona, Spain), July 2011. 

\item[\textbf{[SLM+11b]} ]
  Salvatore Scellato, Ilias Leontiadis, Cecilia Mascolo, Pritwish Basu, Murtaza
  Zafer. Evaluating Temporal Robustness of Mobile Networks. \emph{{IEEE
    Transactions on Mobile Computing}}, 15 November 2011. 
    IEEE Computer Society.


\item[\textbf{[ASW12]} ]
Anders Brodersen, Salvatore Scellato, Mirjam Wattenhofer.
  YouTube Around the World: Geographic Popularity of Videos.
  \emph{{Proceedings of the 21st World Wide Web Conference (WWW 2012)}}, (Lyon,
      France), April 2012.

\item[\textbf{[YSL+12]} ]
Yana Volkovich, Salvatore Scellato, David Laniado, Cecilia Mascolo, Andreas
Kaltenbrunner.
The length of bridge ties: structural and geographic properties of online social
interactions. \emph{{Proceedings of the Sixth International AAAI Conference on Weblogs
and Social Media (ICWSM 2012)}}, (Dublin, Ireland), June 2012. 

\item[\textbf{[NSL+12]} ]
Anastasios Noulas, Salvatore Scellato, Renaud Lambiotte, Massimiliano Pontil,
Cecilia Mascolo.  A tale of many cities: universal patterns in human
urban mobility.  To appear in \emph{PLoS ONE}. 

\item[\textbf{[BNS+12]} ]
Chlo\"{e} Brown, Vincenzo Nicosia, Salvatore Scellato, Anastasios Noulas, Cecilia
Mascolo. 
The Importance of Being Placefriends: Discovering Location-focused Online
Communities. 
\emph{Proceedings of the Fourth Workshop on Online Social Networks (WOSN 2012)},
  co-located with SIGCOMM 2012, (Helsinki, Finland), August 2012. 

\item[\textbf{[KSV+12]} ]
Andreas Kaltenbrunner, Salvatore Scellato, Yana Volkovich, David Laniado, Dave
Currie, Erik J. Jutemar, Cecilia Mascolo. 
Far from the eyes, close on the Web: impact of geographic distance on online
social interactions. 
\emph{Proceedings of the Fourth Workshop on Online Social Networks (WOSN 2012)},
  co-located with SIGCOMM 2012, (Helsinki, Finland), August 2012. 

\item[\textbf{[NSLM12]} ]
Anastasios Noulas, Salvatore Scellato, Neal Lathia, Cecilia Mascolo, A Random
Walk Around the City: New Venue Recommendation in Location-Based Services.
\emph{Proceedings of the Fourth IEEE International Conference on Social
  Computing, (SocialCom 2012)}, 
Amsterdam, The Netherlands, September 2012. 

\end{description}

